Comments (3)
Hi Nguyễn,
You can obtain such files by running extraction scripts mentioned here: https://github.com/j-min/CLIP-Caption-Reward#ms-coco
python scripts/prepro_labels.py --input_json data/dataset_coco.json --output_json data/cocotalk.json --output_h5 data/cocotalk
python scripts/clip_prepro_feats.py --input_json data/dataset_coco.json --output_dir data/cocotalk --images_root datasets/COCO/images --model_type RN50
python scripts/clipscore_prepro_feats.py --input_json data/dataset_coco.json --output_dir data/cocotalk --images_root datasets/COCO/images
from clip-caption-reward.
Thank you very much
from clip-caption-reward.
One more question, when i run the command to train mle
python tools/train_pl.py --cfg configs/phase2/$RL_ID.yml --id $RL_ID
After the sanity check, the code calculate scores and then end without training. Do you know what's wrong here?
from clip-caption-reward.
Related Issues (12)
- Factor in the weigthed reward sum is inconsistent with the paper HOT 2
- pre-training model HOT 4
- Select part of the data set
- onnx version ?
- difference between clip_prepo_feat.py and clipscore_prepo_feat.py HOT 2
- Where is the file 'data/ finecapeval. json'? HOT 1
- Error running python 'clip_prepro_feats.py' HOT 2
- Phase 1 validation throws: shape '[4, -1, 512]' is invalid for input of size 64000 HOT 3
- About language_evaluation HOT 1
- Config file and performance reproduce HOT 11
- phase 2 of clips HOT 2
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from clip-caption-reward.